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378 lines
1.9 MiB
Plaintext
378 lines
1.9 MiB
Plaintext
1 year ago
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/mlabonne/how-to-data-science/blob/main/Improve_ChatGPT_with_Knowledge_Graphs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Improve ChatGPT with Knowledge Graphs\n",
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"\n",
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"❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne)."
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],
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"metadata": {
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"id": "vM5VDCJyAWnC"
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},
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"id": "vM5VDCJyAWnC"
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},
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{
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"cell_type": "code",
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"source": [
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"!pip install -q openai langchain"
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],
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"metadata": {
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"id": "uWz0SMuX5FFp"
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},
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"id": "uWz0SMuX5FFp",
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"import os\n",
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"import openai\n",
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"\n",
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"os.environ['OPENAI_API_KEY'] = \"your OpenAI key\"\n",
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"openai.api_key = os.environ['OPENAI_API_KEY']\n",
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"\n",
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"question = \"When did apple announced the Vision Pro?\"\n",
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"completion = openai.ChatCompletion.create(model=\"gpt-3.5-turbo\",\n",
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" temperature=0,\n",
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" messages=[{\"role\": \"user\",\n",
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" \"content\": question}])\n",
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"print(completion[\"choices\"][0][\"message\"][\"content\"])"
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],
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"metadata": {
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"id": "XpvHDkzNnFLj",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "3d634edc-dbba-4294-8dcc-2ea998e7928e"
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},
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"id": "XpvHDkzNnFLj",
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"As an AI language model, I do not have access to current events or real-time information. However, as of my last training data, Apple has not announced any product called \"Vision Pro.\" It is possible that this product does not exist or has not been announced yet.\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "88a9f2e3-c729-455a-a338-2f83776c1d4c",
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"metadata": {
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"id": "88a9f2e3-c729-455a-a338-2f83776c1d4c",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "f6c9f4d8-387b-4ffc-e200-0c35ed449d8a"
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},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"[('Apple', 'Vision Pro', 'announced'),\n",
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" ('Vision Pro', '2023', 'was announced in')]"
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]
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},
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"metadata": {},
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"execution_count": 3
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}
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],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.indexes import GraphIndexCreator\n",
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"from langchain.chains import GraphQAChain\n",
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"from langchain.prompts import PromptTemplate\n",
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"\n",
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"text = \"Apple announced the Vision Pro in 2023.\"\n",
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"\n",
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"index_creator = GraphIndexCreator(llm=OpenAI(temperature=0))\n",
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"graph = index_creator.from_text(text)\n",
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"graph.get_triples()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eecf2d57-3efa-4b0d-941a-95438d42893c",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"id": "eecf2d57-3efa-4b0d-941a-95438d42893c",
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"outputId": "1db33465-8f43-4294-84e0-0ad481b005aa"
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},
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"<Figure size 2400x1500 with 1 Axes>"
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],
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"image/png": "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},
|
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|
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"metadata": {}
|
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}
|
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],
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"source": [
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"import networkx as nx\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Create graph\n",
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"G = nx.DiGraph()\n",
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"G.add_edges_from((source, target, {'relation': relation}) for source, relation, target in graph.get_triples())\n",
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"\n",
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"# Plot the graph\n",
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"plt.figure(figsize=(8,5), dpi=300)\n",
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"pos = nx.spring_layout(G, k=3, seed=0)\n",
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"\n",
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"nx.draw_networkx_nodes(G, pos, node_size=2000)\n",
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"nx.draw_networkx_edges(G, pos, edge_color='gray')\n",
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"nx.draw_networkx_labels(G, pos, font_size=12)\n",
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"edge_labels = nx.get_edge_attributes(G, 'relation')\n",
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"nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=10)\n",
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"\n",
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"# Display the plot\n",
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"plt.axis('off')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)\n",
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"chain.run(question)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 199
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},
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"id": "KZIs4N5S8e8S",
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"outputId": "af6ba50c-e5f6-40be-adaf-07911dab948f"
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},
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"id": "KZIs4N5S8e8S",
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new GraphQAChain chain...\u001b[0m\n",
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"Entities Extracted:\n",
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"\u001b[32;1m\u001b[1;3m Apple, Vision Pro\u001b[0m\n",
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"Full Context:\n",
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"\u001b[32;1m\u001b[1;3mApple announced Vision ProVision Pro was announced in 2023\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"output_type": "execute_result",
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"data": {
|
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"text/plain": [
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"' Apple announced Vision Pro in 2023.'"
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],
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"application/vnd.google.colaboratory.intrinsic+json": {
|
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"type": "string"
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}
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},
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"metadata": {},
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"execution_count": 5
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"from langchain.graphs.networkx_graph import KnowledgeTriple\n",
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"\n",
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"# Knowledge graph\n",
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"kg = [\n",
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" ('Apple', 'is', 'Company'),\n",
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" ('Apple', 'created', 'iMac'),\n",
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" ('Apple', 'created', 'iPhone'),\n",
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" ('Apple', 'created', 'Apple Watch'),\n",
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" ('Apple', 'created', 'Vision Pro'),\n",
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"\n",
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" ('Apple', 'developed', 'macOS'),\n",
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" ('Apple', 'developed', 'iOS'),\n",
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" ('Apple', 'developed', 'watchOS'),\n",
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"\n",
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" ('Apple', 'is located in', 'USA'),\n",
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" ('Steve Jobs', 'co-founded', 'Apple'),\n",
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" ('Steve Wozniak', 'co-founded', 'Apple'),\n",
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" ('Tim Cook', 'is the CEO of', 'Apple'),\n",
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"\n",
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" ('iOS', 'runs on', 'iPhone'),\n",
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" ('macOS', 'runs on', 'iMac'),\n",
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" ('watchOS', 'runs on', 'Apple Watch'),\n",
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"\n",
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" ('Apple', 'was founded in', '1976'),\n",
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" ('Apple', 'owns', 'App Store'),\n",
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" ('App Store', 'sells', 'iOS apps'),\n",
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"\n",
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" ('iPhone', 'announced in', '2007'),\n",
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" ('iMac', 'announced in', '1998'),\n",
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" ('Apple Watch', 'announced in', '2014'),\n",
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" ('Vision Pro', 'announced in', '2023'),\n",
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"]\n",
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"\n",
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"graph = index_creator.from_text('')\n",
|
||
|
|
"for (node1, relation, node2) in kg:\n",
|
||
|
|
" graph.add_triple(KnowledgeTriple(node1, relation, node2))"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "aa1R_DL-vTgv"
|
||
|
|
},
|
||
|
|
"id": "aa1R_DL-vTgv",
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"# Create directed graph\n",
|
||
|
|
"G = nx.DiGraph()\n",
|
||
|
|
"for node1, relation, node2 in kg:\n",
|
||
|
|
" G.add_edge(node1, node2, label=relation)\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot the graph\n",
|
||
|
|
"plt.figure(figsize=(25, 25), dpi=300)\n",
|
||
|
|
"pos = nx.spring_layout(G, k=2, iterations=50, seed=0)\n",
|
||
|
|
"\n",
|
||
|
|
"nx.draw_networkx_nodes(G, pos, node_size=5000)\n",
|
||
|
|
"nx.draw_networkx_edges(G, pos, edge_color='gray', edgelist=G.edges(), width=2)\n",
|
||
|
|
"nx.draw_networkx_labels(G, pos, font_size=12)\n",
|
||
|
|
"edge_labels = nx.get_edge_attributes(G, 'label')\n",
|
||
|
|
"nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=12)\n",
|
||
|
|
"\n",
|
||
|
|
"# Display the plot\n",
|
||
|
|
"plt.axis('off')\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 1000
|
||
|
|
},
|
||
|
|
"id": "GhVuIxbUwJ1a",
|
||
|
|
"outputId": "a3c0e3a8-b4a3-4baa-93ea-2daf03e352a2"
|
||
|
|
},
|
||
|
|
"id": "GhVuIxbUwJ1a",
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 7500x7500 with 1 Axes>"
|
||
|
|
],
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)\n",
|
||
|
|
"chain.run(question)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 380
|
||
|
|
},
|
||
|
|
"id": "7oGBfjuSuRNm",
|
||
|
|
"outputId": "f4b03c71-c05e-492a-c9ba-21accfb994c8"
|
||
|
|
},
|
||
|
|
"id": "7oGBfjuSuRNm",
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"name": "stdout",
|
||
|
|
"text": [
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\u001b[1m> Entering new GraphQAChain chain...\u001b[0m\n",
|
||
|
|
"Entities Extracted:\n",
|
||
|
|
"\u001b[32;1m\u001b[1;3m Apple, Vision Pro\u001b[0m\n",
|
||
|
|
"Full Context:\n",
|
||
|
|
"\u001b[32;1m\u001b[1;3mApple is Company\n",
|
||
|
|
"Apple created iMac\n",
|
||
|
|
"Apple created iPhone\n",
|
||
|
|
"Apple created Apple Watch\n",
|
||
|
|
"Apple created Vision Pro\n",
|
||
|
|
"Apple developed macOS\n",
|
||
|
|
"Apple developed iOS\n",
|
||
|
|
"Apple developed watchOS\n",
|
||
|
|
"Apple is located in USA\n",
|
||
|
|
"Apple was founded in 1976\n",
|
||
|
|
"Apple owns App StoreVision Pro announced in 2023\u001b[0m\n",
|
||
|
|
"\n",
|
||
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"' Apple announced the Vision Pro in 2023.'"
|
||
|
|
],
|
||
|
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
|
|
"type": "string"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"execution_count": 8
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.16"
|
||
|
|
},
|
||
|
|
"colab": {
|
||
|
|
"provenance": [],
|
||
|
|
"include_colab_link": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|